Abstract
Under the boom of the service robot, the human continuous action recognition becomes an indispensable research. In this paper, we propose a continuous action recognition method based on multi-channel 3D CNN for extracting multiple features, which are classified with KNN. First, we use fragmentary action as training samples which can be identified in the process of action. Then the training samples are processed through the gray scale, improved L-K optical flow and Gabor filter, to extract the characteristics of diversification using a priori knowledge. Then the 3D CNN is constructed to process multi-channel features that are formed into 128-dimension feature maps. Finally, we use KNN to classify those samples. We find that the fragmentary action in continuous action of the identification showed a good robustness. And the proposed method is verified in HMDB-51 and UCF-101 to be more accurate than Gaussian Bayes or the single 3D CNN in action recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, J., Zheng, J., Zhang, S., et al.: A face recognition system based on local binary patterns and support vector machine for home security service robot. In: 2016 9th International Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp. 303–307. IEEE (2016)
Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28(6), 976–990 (2010)
Wagner, A., Bartolein, C., Badreddin, E.: Multi-level human-machine-interaction monitoring and system reconfiguration. In: Rodić, A., Borangiu, T. (eds.) RAAD 2016, pp. 370–377. Springer International Publishing, Heidelberg (2016). doi:10.1007/978-3-319-49058-8_40
Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 9–14. IEEE (2010)
Faria, D.R., Vieira, M., Premebida, C., et al.: Probabilistic human daily activity recognition towards robot-assisted living. In: 2015 24th IEEE International Symposium on IEEE Robot and Human Interactive Communication (RO-MAN), pp. 582–587 (2015)
Vemulapalli, R., Arrate, F., Chellappa, R.: Human action recognition by representing 3D skeletons as points in a lie group. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 588–595 (2014)
Wang, J., Liu, Z., Wu, Y.: Learning actionlet ensemble for 3D human action recognition. In: Human Action Recognition with Depth Cameras, pp. 11–40. Springer International Publishing, Heidelberg (2014). doi:10.1007/978-3-319-04561-0_2
Chen, C., Liu, K., Kehtarnavaz, N.: Real-time human action recognition based on depth motion maps. J. Real-time Image Process. 12(1), 155–163 (2016)
Chaaraoui, A.A., Padilla-López, J.R., Climent-Pérez, P., et al.: Evolutionary joint selection to improve human action recognition with RGB-D devices. Expert Syst. Appl. 41(3), 786–794 (2014)
Zhu, G., Zhang, L., Shen, P., et al.: An online continuous human action recognition algorithm based on the kinect sensor. Sensors 16(2), 161 (2016)
Guo, P., Miao, Z., Shen, Y., et al.: Continuous human action recognition in real time. Multimedia Tools Appl. 68(3), 827–844 (2014)
Eum, H., Yoon, C., Lee, H., et al.: Continuous human action recognition using depth-MHI-HOG and a spotter model. Sensors 15(3), 5197–5227 (2015)
Ji, S., Xu, W., Yang, M., et al.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)
Karpathy, A., Toderici, G., Shetty, S., et al.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568–576 (2014)
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: ICCV (2011)
Soomro, K., Zamir, A.R., Shah, M.: UCF101: A Dataset of 101 Human Action Classes From Videos in The Wild, CRCV-TR-12-01, November 2012
Simoncelli, E.P., Adelson, E.H., Heeger, D.J.: Probability distributions of optical flow. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 1991, pp. 310–315. IEEE (1991)
Tran, D., Bourdev, L., Fergus, R., et al.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489–4497 (2015)
Peng, X., Wang, L., Wang, X., et al.: Bag of visual words and fusion methods for action recognition: comprehensive study and good practice. Comput. Vis. Image Underst. 150, 109–125 (2016)
Yu, S., Cheng, Y., Su, S., et al.: Stratified pooling based deep convolutional neural networks for human action recognition. Multimedia Tools Appl. 1–16 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yu, G., Li, T. (2017). Recognition of Human Continuous Action with 3D CNN. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-68345-4_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68344-7
Online ISBN: 978-3-319-68345-4
eBook Packages: Computer ScienceComputer Science (R0)